English

# Item

ITEM ACTIONSEXPORT
Deep-Learning Continuous Gravitational Waves

Dreissigacker, C., Sharma, R., Messenger, C., & Prix, R. (2019). Deep-Learning Continuous Gravitational Waves. Physical Review D, 100 (4): 044009. doi:10.1103/PhysRevD.100.044009.

Item is

### Basic

show hide
Genre: Journal Article

### Files

show Files
hide Files
:
1904.13291.pdf (Preprint), 4MB
Name:
1904.13291.pdf
Description:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
-
-
:
PhysRevD.100.044009.pdf (Publisher version), 919KB
Name:
PhysRevD.100.044009.pdf
Description:
Open Access
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
-
-

show

### Creators

show
hide
Creators:
Dreissigacker, Christoph, Author
Sharma, Rahul, Author
Messenger, Chris1, Author
Prix, Reinhard2, Author
Affiliations:
1Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011
2Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_2630691

### Content

show
hide
Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM
Abstract: We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from $20\,\mathrm{Hz}$ to $1000\,\mathrm{Hz}$: an easy' search using $T=10^5\,\mathrm{s}$ of data, and a harder' search using $T=10^6\,\mathrm{s}$. Detection probability $p_\mathrm{det}$ is measured on a signal population for which matched filtering achieves $p_\mathrm{det}=90\%$ in Gaussian noise. In the easiest test case ($T=10^5\,\mathrm{s}$ at $20\,\mathrm{Hz}$) the DNN achieves $p_\mathrm{det}\sim88\%$, corresponding to a loss in sensitivity depth of $\sim5\%$ versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until $p_\mathrm{det}\sim13\%$ and a loss of $\sim 66\%$ in sensitivity depth in the hardest case ($T=10^6\,\mathrm{s}$ at $1000\,\mathrm{Hz}$). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.

### Details

show
hide
Language(s):
Dates: 2019-04-302019-05-032019
Publication Status: Published in print
Pages: 11 pages, 17 figures, 7 tables; changed author list
Publishing info: -
Rev. Method: -
Identifiers: arXiv: 1904.13291
URI: http://arxiv.org/abs/1904.13291
DOI: 10.1103/PhysRevD.100.044009
Degree: -

show

show

show

### Source 1

show
hide
Title: Physical Review D
Source Genre: Journal
Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 100 (4) Sequence Number: 044009 Start / End Page: - Identifier: -